When people with weak immune system are infected with pathogenic microorganisms, the infection (all infections are not difficult to treat) will be very difficult to treat and can result in fatality. Thus, efforts to find targets for developing more effective anti-pathogenic drugs using the genomic information of pathogenic microorganisms have been actively conducted. However, it is often difficult to identify a gene candidate for drug targeting to kill pathogenic microorganisms, and it is technically difficult to determine the lethality of the relevant gene while deleting all the single genes of pathogenic microorganisms. Drug targets are mostly determined by the complex interactions between cellular components, rather than by single genes, and the deletion of a plurality of genes shows lethality, even when each gene has no lethality.
Accordingly, in order to develop effective drugs targeting pathogenic microorganisms, it is very important to understand the cellular mechanisms and interactions between microbial cellular components. Thus, the construction of metabolites and metabolic networks through the development of genomic information and functional genetics is of increasing importance in understanding the interactions between genes and proteins constituting cellular components and in constructing metabolic networks to develop effective drugs.
In fact, when new metabolic pathways, which are not found in mammalian cells, are identified in pathogenic microorganisms using metabolic network information through genomic information, it is possible to develop treatment methods targeting such metabolic characteristics to specifically attack pathogenic cells without causing side effects in human cells. If this pathogen specific metabolic pathway is identified as essential for the survival of pathogenic microorganisms, it is possible to develop a drug to inhibit the relevant metabolic pathway. It is considered that, once a drug against pathogenic microorganisms is developed, a drug for suppressing other similar pathogenic microorganisms can be easily obtained using compounds similar thereto. Analysis and prediction technology based on a metabolic network has recently become feasible with the increasing availability of genomic information. Particularly, with the development of the mathematical representation of the organisms' metabolism and its simulation using optimization techniques, it is becoming possible to predict metabolic pathway reactions occurring after deletion or addition of specific genes (Lee et al., Trend. Biotechnol., 23:349, 2005). The metabolic flux analysis (MFA) technique shows the ideal metabolic flux of cells and allows for simulation and prediction of the cell behaviors, even though it does not require physiological parameters (Papin, J. et al., Nature Reviews Molecular Cell Biology, 6:99, 2005). Also, metabolic flux analysis is a technology of determining a change in internal metabolic flux by measuring the various coefficients of metabolic reaction equations and the production and consumption of metabolites, and is based on the assumption of a quasi-steady state.
Metabolic flux analysis is used to obtain an ideal metabolic flux space that cells can reach using only the mass balance equations and cell composition information and aims to maximize or minimize specific objective functions through a optimization method (e.g., the maximization of biomass formation or the minimization of metabolic adjustment by specific perturbation). In addition, metabolic flux analysis can be generally used to calculate the lethality of a specific gene for desired metabolites through strain improvement and to understand the metabolic pathway characteristics of strains. Also, various studies, which apply the metabolic flux analysis technique to predict metabolic flux changes occurring after the deletion or addition of genes, have been reported.
Previously, inventors developed an in-silico method for improving organisms using the flux sum of metabolites, in which the relationship of a specific metabolite with the production of useful substances can be predicted, and an organism having an increased ability to produce useful substances can be developed by introducing or amplifying genes expressing enzymes associated with specific metabolites (Korean Patent 10-655495).
US Patent Publication US2002/0168654 A1 discloses a method for in silico modeling of cellular metabolism, which can improve a flux balance analysis (FBA) model through specific constraints. In this patent publication, there was an attempt to identify the minimal set of metabolic reactions capable of supporting cellular growth, through constraints influencing the cell growth, but there was a problem in identifying synthetic lethal genes because too many combinations of mutations exist.
Thus, in the art to which the present invention pertains, there is a need to develop a method, which can precisely predict target genes in pathogenic microorganisms by examining complex microbial metabolism using a metabolic flux analysis technique in an overall viewpoint, other than using strain manipulation based on partial metabolic information, and determining the effects of manipulation of a specific gene on overall metabolic flux.
Accordingly, the present inventors have made extensive efforts to develop a method capable of efficiently predicting target genes of a pathogenic microorganism. As a result, the present inventors have found that the target genes essential for the growth of the microorganism can be screened by inactivating the consumption reaction of each metabolite in a metabolic network model, analyzing the metabolic flux of each of the metabolites, and screening for metabolites showing a decrease in cell growth rate compared to metabolites whose consumption reaction is not inactivated.